Information about Test

  1. Vanishing gradient problem

    training artificial neural networks with gradient-based learning methods and backpropagation. In such methods, each of the neural network's weights receives

  2. Artificial intelligence

    deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses convolutional neural networks

  3. Comparison gallery of image scaling algorithms

    Dengwen Zhou; Xiaoliu Shen. "Image Zooming Using Directional Cubic Convolution Interpolation". Retrieved 13 September 2015. Shaode Yu; Rongmao Li; Rui

  4. Feature learning

    are learned using labeled input data. Examples include supervised neural networks, multilayer perceptron and (supervised) dictionary learning. In unsupervised

  5. Data science

    Artificial neural network Autoencoder Deep learning DeepDream Multilayer perceptron RNN LSTM GRU Restricted Boltzmann machine GAN SOM Convolutional neural network

  6. Word2vec

    used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec

  7. History of artificial neural networks

    artificial neural networks (ANN) began with Warren McCulloch and Walter Pitts (1943) who created a computational model for neural networks based on algorithms

  8. Darkforest

    developed by Facebook, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its

  9. DexNet

    Dex-net is a robotic manipulator. It uses a Grasp Quality Convolutional Neural Network to learn how to grasp unusually shaped objects. Dex-net was developed

  10. Supervised learning

    Geman, E. Bienenstock, and R. Doursat (1992). Neural networks and the bias/variance dilemma. Neural Computation 4, 1–58. G. James (2003) Variance and